Classifiers and thresholds for evaluating a situation

Currently, your dashboard is very basic. It displays the count and chart for three metrics:

  1. The number of orders received today.
  2. The number of orders completed today.
  3. The number of orders currently being processed.

Part 4 of the tutorial focuses on adding additional information to your dashboard so you can know:

  • Whether the numbers you're seeing are within expectations.
  • Whether a situation is improving or getting worse.

In other words, let's add more contextual information that will enable you to better evaluate the risks in a situation, for example, is an order taking longer than usual to be processed, does the data your dashboard is displaying reflect a normal or a dangerous situation?

About classifiers

Classifiers define changes of state and the visual clues (for example, red, yellow green) to provide the user to identify those changes. Classifiers are:

  • The building blocks of status evaluations. You use classifiers to define how evaluations will change from one state to the other.
  • Defined at the deployment level – one classifier may be used for more than one attribute.

About thresholds

Thresholds are associated with an entity, just like attributes and relation attributes. This way a classifier could be shared by many different thresholds. For example, the definition of a "normal" status classifier (green light) may correspond to very different values based on the entity.  


Let's say you have an entity that tracks fuel levels and another that monitors speed. The values (and direction of status changes) would be different for each entity's threshold but they could share the same classifier.

In the figure below:

  • Classifiers – define the colors of the risk levels and their corresponding labels – Normal, Warning, Danger. 
  • Thresholds – define the actual values of the attributes that will trigger a change in status

Classifiers and Thresholds

There is a difference in evaluating situations rather than simply reporting numbers. Evaluations provide an analysis of a situation that – without DI – might only come from years of experience.

Numbers provide facts. Thresholds and evaluations provide opinions about facts.

In your application, the next logical step is to learn how to define evaluations and thresholds.

First, you need to access more realistic data and expand your model to integrate additional entities. That comes next.

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